Hurricane Wind Research Articles

Bivariate cubic normal distribution for non-Gaussian problems

Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. However, conventional parameter-based bivariate distribution models are generally developed for specific types of random variables, limiting their flexibility and applicability. A flexible bivariate distribution model is proposed, in which the joint cumulative distribution function is derived with the probability equivalently expressed as the summation of three basic probabilities corresponding to simple functions. These three basic probabilities are calculated with the aid of univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN model accurately models both typical theoretical distributions and the JCDF of practical datasets, offering a significant improvement over existing models. Furthermore, the proposed BCN distribution has been utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.

Performance characterization of a wireless sensors network system (WSNS) for measurements of hurricane wind effects on structures

This paper presents a new wireless sensors network system (WSNS) designed for high-resolution absolute pressure measurements, wind speed, and direction. The system is tailored to assess the impact of hurricane winds on residential structures, both in laboratory settings and field environments. Importantly WSNS stands out for its unique ability to provide waterproof, surface-mounted external pressure measurements. The system's performance is evaluated during deployment on a full-scale house model at the Wall of Wind facility. The WSNS sensors were installed on different surfaces of a single-story residential building model. The sensor locations mirrored the locations of surface pressure taps connected to a Scanivalve differential pressure measurement system. Due to the size and shape of the WSNS pressure module, a casing effect was observed, which may result in pressure offsets under certain wind speeds and directions, depending on the sensor's location and the conditions, including dry and light rain (50 mm per hour). The comparison between WSNS and Scanivalve indicates that the sensor's casing geometry does not cause significant differences in the time-averaged measurements for low-turbulence regions. Conversely, this is not true for high-turbulence regions, which should be marked for future deployments using smaller surface-mounted pressure taps.

Projecting Future Tropical Cyclone Frequencies by Combining Uncertain Empirical Estimates of Baseline Frequencies with Climate Model Estimates of Change

Various studies have given projections for how frequencies of tropical cyclones (TCs) might change under climate change. In this study, we combine a set of such projections with uncertain estimates of frequencies of tropical cyclones in a baseline climate to produce probabilistic projections of tropical cyclone frequencies for the next 50 years. The novel aspect of our projections is the inclusion of baseline uncertainty. We consider frequencies of Saffir-Simpson Hurricane Wind Scale category 0-5 and category 4-5 storms for the six major tropical cyclone basins. We find that in several cases the means and medians of the frequency of category 0-5 storms are projected to decrease, but that increasing uncertainty nevertheless leads to increases in the likelihood of high rates of TC activity in the future. We then show how the variance of the distributions of uncertainty can be decomposed into terms due to baseline uncertainty and climate change uncertainty. We use this decomposition to determine the year in which climate change uncertainty overtakes baseline uncertainty. Over the next 20 years we find that in some basins baseline uncertainty dominates and in other basins climate change uncertainty dominates. We are able to relate these variations between basins to the coefficients of variation of the baseline and climate change inputs. Finally, we quantify how climate change affects estimates of near-term TC frequency, including the extent to which it increases the uncertainty. These results help us understand two of the major sources of uncertainty in estimates of tropical cyclone behaviour now and in the future, and the role climate change is playing in changing that behaviour. They also demonstrate how estimates of TC change can be combined with observations to create projections of future TC climate.

Estuarine hurricane wind can intensify surge-dominated extreme water level in shallow and converging coastal systems

Estuarine hurricane wind can intensify surge-dominated extreme water level in shallow and converging coastal systems

Empirical hurricane fragility assessment of elevated and slab-on-grade residential houses

The widespread hurricane-induced damage to residential houses in the United States leads to severe social and economic impacts. Recent community-wide efforts by researchers within the natural hazards community to collect and curate post-hazard damage data have advanced the potential to assess structural vulnerability during extreme wind events. These efforts have led to data re-use and further analysis for detailed evaluation, allowing for the comparison of the performance of elevated versus slab-on-grade single-family residential houses during hurricanes. The empirically derived fragility functions from these damage assessment data offer a unique opportunity to evaluate the vulnerability of constructed buildings. This paper presents the development of empirical fragility functions for elevated and slab-on-grade residential houses, along with their building envelope components, when subjected to hurricane winds. It assesses the post-hurricane occupiability and the annual probability of failure for both elevated and slab-on-grade houses. Utilizing post-hurricane building performance data collected from 618 elevated and 1188 slab-on-grade single-family residential houses impacted by Hurricanes Irma and Harvey in 2017, we analyzed the relationship of damage to the estimated peak 3-sec gust wind speed at each house. The analysis yields the assignment of each house to one of five damage state categories, from which we developed the fragility functions of buildings and building components in accordance with FEMA's HAZUS Hurricane model. Results showed that the developed empirical fragility functions for building envelope components of elevated houses have a higher probability of failure compared to those installed on slab-on-grade houses. Additionally, the likelihood of elevated houses being unoccupiable after a hurricane is significantly higher than that of slab-on-grade houses in both Florida and Texas. For residential buildings constructed in the state of Florida, this study finds that elevated houses have a 39 %–74 % higher annual probability of failure due to hurricane wind compared to slab-on-grade houses for the different considered damage states.

Potential for mitigating hurricane wind impact on informally-constructed homes in Puerto Rico under current and future climate scenarios

This study investigates the resilience of informally-constructed light-frame timber houses in Puerto Rico, a region where households with limited resources face significant risks from climate hazards, notably hurricanes. This study conducts a component-based, performance-based wind engineering assessment of informally-constructed house typologies, defined based on extensive fieldwork, under both existing and projected future climate conditions. Key findings highlight the effectiveness of certain mitigation strategies, such as reinforcing roof-to-wall connections, in significantly reducing the probability of failure. Fully-mitigated cases, which involve applying mitigation measures to the roof envelope, roof-to-wall connections, and shear walls, exhibited annual probabilities of failure that are much closer to, but do not necessarily meet, the threshold targeted by American building standards (i.e., ASCE 7). The results also show a dramatic increase in probability of failure of these houses projected by the adopted climate change model scenarios, driven by the increased frequency and intensity of hurricanes in Puerto Rico. Results from feedback from those working in the informal construction sector also identify challenges hindering the effective implementation of mitigation measures in Puerto Rican communities, including a lack of knowledge about how to implement the mitigation strategies and barriers related to real and perceived costs. Taken together these results underscore the urgent need for changes in building practices and revising building standards and suggesting potentially feasible mitigation strategies to improve those practices.

In-event surrogate model training for regional transmission tower-line system dynamic responses prediction in realistic hurricane wind fields

Surrogate models have shown improved accuracy in predicting infrastructure responses during dynamic loadings. However, training a surrogate model for complex loading inputs across the entire hazard region remains challenging. This study provides insight into the training of surrogate models to estimate the responses of transmission tower-line structures in a coupled high-dimensional and high-resolution wind field and presents innovative methods for addressing these challenges. Four data- and physics-based spatial-temporal decoupling sampling methods are employed and cross-compared to obtain the most representative in-event wind profiles for training the surrogate model. Long Short-Term Memory (LSTM) is utilised as the surrogate model framework to predict the dynamic responses of the structure during the 2017 Hurricane Harvey. The accuracy and robustness of two transmission tower-line structure configuration surrogate models are validated by comparing the predictions with finite element analyses by using randomly distributed temporal and geospatial wind profiles throughout the hurricane. Finally, a single LSTM surrogate model is developed, trained by applying the full reference wind speed range of Hurricane Harvey for the regional-scale structural performance evaluation of the transmission tower-line system. The results demonstrate that the proposed surrogate model training methodology is general and can be applied to regional-scale structural performance evaluations.

Hurricanes pose a substantial risk to New England forest carbon stocks.

Nature-based climate solutions (NCS) are championed as a primary tool to mitigate climate change, especially in forested regions capable of storing and sequestering vast amounts of carbon. New England is one of the most heavily forested regions in the United States (>75% forested by land area), and forest carbon is a significant component of climate mitigation policies. Large infrequent disturbances, such as hurricanes, are a major source of uncertainty and risk for policies relying on forest carbon for climate mitigation, especially as climate change is projected to alter the intensity and extent of hurricanes. To date, most research into disturbance impacts on forest carbon stocks has focused on fire. Here, we show that a single hurricane in the region can down between 121 and 250 MMTCO2e or 4.6%-9.4% of the total aboveground forest carbon, much greater than the carbon sequestered annually by New England's forests (16 MMTCO2e year-1). However, emissions from hurricanes are not instantaneous; it takes approximately 19 years for downed carbon to become a net emission and 100 years for 90% of the downed carbon to be emitted. Reconstructing hurricanes with the HURRECON and EXPOS models across a range of historical and projected wind speeds, we find that an 8% and 16% increase in hurricane wind speeds leads to a 10.7- and 24.8-fold increase in the extent of high-severity damaged areas (widespread tree mortality). Increased wind speed also leads to unprecedented geographical shifts in damage, both inland and northward, into heavily forested regions traditionally less affected by hurricanes. Given that a single hurricane can emit the equivalent of 10+ years of carbon sequestered by forests in New England, the status of these forests as a durable carbon sink is uncertain. Understanding the risks to forest carbon stocks from disturbances is necessary for decision-makers relying on forests as a NCS.

We’ve Already Seen Category 6 Hurricanes—Now Scientists Want to Make It Official

Intensifying storms may warrant a new category of hurricane wind speeds.

The growing inadequacy of an open-ended Saffir–Simpson hurricane wind scale in a warming world

Global warming increases available sensible and latent heat energy, increasing the thermodynamic potential wind intensity of tropical cyclones (TCs). Supported by theory, observations, and modeling, this causes a shift in mean TC intensity, which tends to manifest most clearly at the greatest intensities. The Saffir-Simpson scale for categorizing damage based on the wind intensity of TCs was introduced in the early 1970s and remains the most commonly used metric for public communication of the level of wind hazard that a TC poses. Because the scale is open-ended and does not extend beyond category 5 (70 m/s windspeed or greater), the level of wind hazard conveyed by the scale remains constant regardless of how far the intensity extends beyond 70 m/s. This may be considered a weakness of the scale, particularly considering that the destructive potential of the wind increases exponentially. Here, we consider how this weakness becomes amplified in a warming world by elucidating the past and future increases of peak wind speeds in the most intense TCs. A simple extrapolation of the Saffir-Simpson scale is used to define a hypothetical category 6, and we describe the frequency of TCs, both past and projected under global warming, that would fall under this category. We find that a number of recent storms have already achieved this hypothetical category 6 intensity and based on multiple independent lines of evidence examining the highest simulated and potential peak wind speeds, more such storms are projected as the climate continues to warm.

Spatiotemporal changes in hurricane-force wind risk assessment in the Yucatan Peninsula, Mexico

Tropical cyclones are one of the most dangerous natural phenomena. These extreme events involve various hazards, such as strong winds, severe precipitation, storm surge, flooding, and landslides. In Mexico, tropical cyclones are the most frequent natural threats and have a high cost to affected populations. This research aimed to characterise the spatial and temporal changes in risk associated with hurricane winds on the Yucatan Peninsula. This effort included a comprehensive analysis of three integral risk components (hazard, vulnerability, and exposure) for three distinct time intervals (1950–2000, 1950–2010, and 1950–2020). This analytical process was executed utilising a fine-resolution hexagonal grid. Hazard was estimated by calculating the probabilities of occurrence of winds related to various hurricane categories after estimating wind fields from the International Best Track Archive for Climate Stewardship (IBTrACS) data with a parametric model. Vulnerability was approximated by constructing indicators with sociodemographic data from the National Population and Housing Census issued by Mexico’s National Institute of Statistics and Geography. With these indicators, a factor analysis was performed, and a weighted index was constructed. Finally, exposure was estimated from population density. Each of these indices was aggregated at the hexagonal level, allowing the calculation of the risk associated with hurricane-force wind. The results showed high-risk levels associated with high-hazard levels, e.g. in coastal areas such as the Riviera Maya. Similarly, high-risk levels are related to high marginalisation, i.e. vulnerability, in the northeastern zone of the Yucatan Peninsula. The increased frequency of tropical cyclones combined with high population densities has recently led to higher risk levels in this region of Mexico.

Development and Application of a GNSS-R Error Model for Hurricane Winds

Development and Application of a GNSS-R Error Model for Hurricane Winds

Влияние опасных гидрометеорологических явлений на эксплуатацию транспортного перехода через Керченский пролив

The article considers the natural and climatic conditions of the Kerch Strait, the Taman Peninsula and the Kerch Peninsula through which passes the transport crossing that connects the Republic of Crimea with the Russian mainland. The paper reflects the main features of the formation of dangerous hydrometeorological phenomena in the Kerch Strait. Hydrometeorological conditions of formation of ice and wind-wave regimes and other dangerous hydrological and hydrometeorological phenomena in the Kerch Strait are highlighted based on the coastal and expedition observations, measurements of currents at automatic buoy stations, numerical modeling of water dynamics in the Kerch Strait for different types of atmospheric effects on the grid with high spatial resolution. The research shows that in the coastal zone and on the Kerch Strait water area periodically there are dangerous hydrometeorological phenomena, such as ice blockages on the bridge piers, strong storm or hurricane wind, heavy precipitation, fogs, which significantly worsen the visibility and complicate marine cargo transportation in the Strait, thunderstorms and tornados. The data presented in this work may be the basis for the assessment of the current state of the environment within the environmental support of the projects, as well as the subsequent ice and hydrometeorological monitoring in the area of the Crimean bridge.

A Space-Time Variational Method for Retrieving Upper-Level Vortex Winds from GOES-16 Rapid Scans over Hurricanes

A space-time variational method is developed for retrieving upper-level vortex winds from geostationary satellite rapid infrared scans over hurricanes. In this method, new vortex-flow-dependent correlation functions are formulated for the radial and tangential components of the vortex wind. These correlation functions are used to construct the background error covariance matrix and its square root matrix. The resulting square root matrix is then employed to precondition the cost function, constrained by an advection equation formulated for rapidly scanned infrared image movements. This newly formulated and preconditioned cost function is more suitable for deriving upper-level vortex winds from GOES-16 rapid infrared scans over hurricanes than the cost function in the recently adopted optical flow technique. The new method was applied to band-13 (10.3 µm) brightness temperature images scanned every min from GOES-16 over Hurricanes Laura on 27 August 2020 and Hurricanes Ida on 29 August 2021. The retrieved vortex winds were shown to not only be much denser than operationally produced atmospheric motion vectors (AMVs) but also more rotational and better organized around the eyewall than the super-high-resolution AMVs derived from optical-flow technique. By comparing their component velocities (projected along radar beams) with limited radar velocity observations available near the cloud top, the vortex winds retrieved using the new method were also shown to be more accurate than the super-high-resolution AMVs derived from the optical-flow technique. The new method is computationally efficient for real-time applications and potentially useful for hurricane wind nowcasts. Furthermore, the combined use of VF-dependent covariance functions and imagery advection equation is not only novel but was also found to be critically important for the improved performance of the method. This finding implies that similar combined approaches can be developed with improved performance for retrieving vortex flows rapidly scanned using other types of remote sensing on different scales, such as tornadic mesocyclones rapidly scanned by phased-array radars.

Statistical Characteristics of Hurricane Winds over Georgia for the Period 1961–2022

Statistical Characteristics of Hurricane Winds over Georgia for the Period 1961–2022

The Impact of Projected Changes in Hurricane Frequencies on U.S. Hurricane Wind and Surge Damage

Abstract We use a simple risk model for U.S. hurricane wind and surge economic damage to investigate the impact of projected changes in the frequencies of hurricanes of different intensities due to climate change. For average annual damage, we find that changes in the frequency of category-4 storms dominate. For distributions of annual damage, we find that changes in the frequency of category-4 storms again dominate for all except the shortest return periods. Sensitivity tests show that accounting for landfall, uncertainties, and correlations leads to increases in damage estimates. When we propagate the distributions of uncertain frequency changes to give a best estimate of the changes in damage, the changes are moderate. When we pick individual scenarios from within the distributions of frequency changes, we find a significant probability of much larger changes in damage. The inputs on which our study depends are highly uncertain, and our methods are approximate, leading to high levels of uncertainty in our results. Also, the damage changes we consider are only part of the total possible change in hurricane damage due to climate change. Total damage change estimates would also need to include changes due to other factors, including possible changes in genesis, tracks, size, forward speed, sea level, rainfall, and exposure. Nevertheless, we believe that our results give important new insights into U.S. hurricane risk under climate change. Significance Statement We investigate how changes in the frequencies of hurricanes of different intensities as a result of climate change may contribute to changes in U.S. economic damage due to wind and surge. We find that economic damage will likely increase as a result of projected increases in the frequency of landfalling hurricanes. Analysis of our results shows that increases in the frequency of category-4 storms are the main driver of the changes. Our best estimate results, based on a multimodel ensemble, give modest increases in damage, but within the ensemble there are individual scenarios that give much larger increases in damage. The large range of individual damage estimates is a motivation for continuing efforts to reduce the uncertainty around hurricane projections under climate change.

Analysis of criticality level of mangrove land in Rawa Aopa Watumohai National Park, Southeast Sulawesi Province

As part of the life support system, the mangrove ecosystem in the Rawa Aopa Watumohai National Park (TNRAW) has an ecological function as a spawning ground for fish in the waters, a filter for sea water intrusion into the mainland, and an absorber of heavy metals that are harmful to life, habitat for wildlife. migratory bird shelters; as well as barrier to coastal abrasion, hurricane winds and tsunamis. Aim of this study were to determine the critical level of mangroves using the terrestrial method (field survey) and to formulate the factors causing the criticality of mangroves in TNRAW. This research uses vegetation and descriptive analysis. The results of this study indicate that there are four families and eight species of mangroves in the research location. The four families are Meliaceae, Rhizophoraceae, Avicenniaceae and Combretacea with the dominating family was Rhizophoracea. Mangroves species found were Xylocarpus granatum K., Bruguiera gymnorrhiza L., Rhizophora mucronata Bl., Rhizophora apiculata L., Ceriops decandra, Ceriops tagal, Avicennia alba and Lumnitzera racemosa. The highest and lowest mangrove species diversity indexes in TNRAW were the seedling and tree strata, respectively with values 1,733 and 1,570. For the critical level of mangrove land measured at the four research stations, it shows that the mangrove ecosystem at the four research station is in a damaged condition. Causal factors of mangrove land critical in TNRAW were economic factors, education and skills, and weak of supervision of the authorities.

Hurricane Wind Loss Estimation for Puerto Rico and the US Virgin Islands: Model Implementation and Validation

Hurricane Wind Loss Estimation for Puerto Rico and the US Virgin Islands: Model Implementation and Validation

Hazus Hurricane Wind Model for the US Caribbean Territories: Hazard Modeling and Development of Residential Damage Functions

Hazus Hurricane Wind Model for the US Caribbean Territories: Hazard Modeling and Development of Residential Damage Functions

Hurricane wind regimes for forests of North America

Despite the ubiquity of tropical cyclones and their impacts on forests, little is known about how tropical cyclone regimes shape the ecology and evolution of tree species. We used a simple meteorological model (HURRECON) to estimate wind fields from hurricanes in the Western North Atlantic and Eastern North Pacific tropical cyclone basins from storms occurring between 1851 and 2022. We characterize how the intensity and frequency of hurricanes differ among geographically distinct hurricane regimes and define four hurricane regimes for North America (Continental, Inland, Coastal, and Fringe). Along this coastal-to-inland gradient, we found major differences in the frequency and intensity of hurricane wind regimes. The Fringe regime experiences category 1 winds relatively frequently [return period (RP) 25 y], whereas the Inland regime experiences category 1 winds very infrequently (RP ~3,000 y). We discuss how species traits related to tree windfirmness, such as mechanical properties and crown traits, may vary along hurricane regime gradients. Quantitative characterization of forest hurricane regimes provides a critical step for understanding the evolutionary and ecological role of hurricane regimes in wind-prone forests.